Engineering the application of machine learning in an IDS based on IoT traffic flow

Internet of Things (IoT) devices are now widely used, enabling intelligent services that, in association with new communication technologies like the 5G and broadband internet, boost smart-city environments. Despite their limited resources, IoT devices collect and share large amounts of data and are connected to the internet, becoming an attractive target for malicious actors. This work uses machine learning combined with an Intrusion Detection System (IDS) to detect possible attacks. Due to the limitations of IoT devices and low latency services, the IDS must have a specialized architecture. Furthermore, although machine learning-based solutions have high potential, there are still challenges related to training and generalization, which may impose constraints on the architecture. Our proposal is an IDS with a distributed architecture that relies on Fog computing to run specialized modules and use deep neural networks to identify malicious traffic inside IoT data flows. We compare our IoT-Flow IDS with three other architectures. We assess model generalization using test data from different datasets and evaluate their performance in terms of Recall, Precision, and F1-Score. Results confirm the feasibility of flowbased anomaly detection and the importance of network traffic segmentation and specialized models in the AI-based IDS for IoT.info:eu-repo/semantics/publishedVersio

Towards evaluation design for smart city development

Smart city developments integrate digital, human, and physical systems in the built environment. With growing urbanization and widespread developments, identifying suitable evaluation methodologies is important. Case-study research across five UK cities - Birmingham, Bristol, Manchester, Milton Keynes and Peterborough - revealed that city evaluation approaches were principally project-focused with city-level evaluation plans at early stages. Key challenges centred on selecting suitable evaluation methodologies to evidence urban value and outcomes, addressing city authority requirements. Recommendations for evaluation design draw on urban studies and measurement frameworks, capitalizing on big data opportunities and developing appropriate, valid, credible integrative approaches across projects, programmes and city-level developments

IoT-liiketoiminnan mallintaminen

Our world is becoming increasingly digitized. Digitalization has changed and is changing business models at accelerating pace and creating new revenue and value-producing opportunities. We are now witnessing the age where the digital technologies are harnessed for our advantage - as the physical technologies were harnessed in the first industrial revolution. Still, the digital world and the physical world are separated from each other. This is the one significant issue, that the Internet of Things (IoT) is about to change. The vision of the IoT is to connect people and devices and produce a vast variety of new goods and services. As the IoT is a novel phenomenon, it can be a difficult concept to define. It can be difficult to create a comprehensive understanding on what the IoT is and what kind opportunities it has to offer. In addition, The IoT is a complex phenomenon in terms of monetization. It can be difficult to create a comprehensive understanding on where the real value of the IoT comes from. The goal of this study is to to create a framework of possible IoT business opportunities for the target company. This is done by creating a conceptualization that unfolds the different roles there are in IoT business for the target company to take or aim for. In addition to the conceptualization, there is also a need to create better understanding of the customership and value proposition related to the IoT business, and recognize the most important barriers of adoption and capabilities required for managing the barriers of adoption.Digitalisaatio on muuttanut ja muuttaa liiketoimintamalleja kiihtyvällä vauhdilla luoden uusia mahdollisuuksia arvontuotolle. Todistamme nyt aikakautta, jossa digitaaliset teknologiat valjastetaan käyttöön, kuten fyysiset teknologiat valjastettiin ensimmäisessä teollisessa vallankumouksessa. Siltikin digitaalinen ja fyysinen maailma ovat olleet tähän asti erossa toisistaan. Tämä on merkittävin asia, jonka esineiden internet tulee muuttamaan. Esineiden internetin visiona on yhdistää ihmiset ja laitteet ja luoda laaja valikoima uusia tavaroita ja palveluita. Koska esineiden internet on uusi ilmiö, sen määritteleminen voi olla vaikeaa. On haastavaa luoda kattavaa käsitystä siitä, mitä esineiden internet on ja millaisia mahdollisuuksia se tarjoaa. Lisäksi esineiden internet on minimutkainen ilmiö kaupallistamisen kannalta. On haastavaa luoda kattavaa käsitystä mistä esineiden internetin todellinen arvo tulee. Tämän opinnäytteen tavoitteena on luoda viitekehys, jonka avulla kohdeyritys voi paremmin hahmottaa esineiden internetin tarjoamia liiketoimintamahdollisuuksia. Tämä mahdollistetaan hahmottamalla erilaiset roolit, joihin kohdeyritys voi asettua. Viitekehyksen lisäksi opinnäytteen tavoitteena on luoda parempi ymmärrys IoT-liiketoimintaan liittyvistä asiakkuuksista ja arvolupauksista, sekä tunnistaa tärkeimmät käyttöönoton esteet sekä tarvittavat kyvykkyydet niiden hallitsemiseksi

Geospatial Data Management Research: Progress and Future Directions

Without geospatial data management, today´s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis

Digital Twins: A Meta-Review on Their Conceptualization, Application, and Reference Architecture

The concept of digital twins (DTs) is receiving increasing attention in research and management practice. However, various facets around the concept are blurry, including conceptualization, application areas, and reference architectures for DTs. A review of preliminary results regarding the emerging research output on DTs is required to promote further research and implementation in organizations. To do so, this paper asks four research questions: (1) How is the concept of DTs defined? (2) Which application areas are relevant for the implementation of DTs? (3) How is a reference architecture for DTs conceptualized? and (4) Which directions are relevant for further research on DTs? With regard to research methods, we conduct a meta-review of 14 systematic literature reviews on DTs. The results yield important insights for the current state of conceptualization, application areas, reference architecture, and future research directions on DTs

QoE in IoT: a vision, survey and future directions

\ua9 The Author(s) 2021. The rapid evolution of the Internet of Things (IoT) is making way for the development of several IoT applications that require minimal or no human involvement in the data collection, transformation, knowledge extraction, and decision-making (actuation) process. To ensure that such IoT applications (we term them autonomic) function as expected, it is necessary to measure and evaluate their quality, which is challenging in the absence of any human involvement or feedback. Existing Quality of Experience (QoE) literature and most QoE definitions focuses on evaluating application quality from the lens of human receiving application services. However, in autonomic IoT applications, poor quality of decisions and resulting actions can degrade the application quality leading to economic and social losses. In this paper, we present a vision, survey and future directions for QoE research in IoT. We review existing QoE definitions followed by a survey of techniques and approaches in the literature used to evaluate QoE in IoT. We identify and review the role of data from the perspective of IoT architectures, which is a critical factor when evaluating the QoE of IoT applications. We conclude the paper by identifying and presenting our vision for future research in evaluating the QoE of autonomic IoT applications

A Deep Learning-Based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things Using Fog Computing

With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big data-driven medical applications have become even more important. The biomedical data produced is highly confidential and private. Unfortunately, conventional health systems cannot support such a colossal amount of biomedical data. Hence, data is typically stored and shared through the cloud. The shared data is then used for different purposes, such as research and discovery of unprecedented facts. Typically, biomedical data appear in textual form (e.g., test reports, prescriptions, and diagnosis). Unfortunately, such data is prone to several security threats and attacks, for example, privacy and confidentiality breach. Although significant progress has been made on securing biomedical data, most existing approaches yield long delays and cannot accommodate real-time responses. This paper proposes a novel fog-enabled privacy-preserving model called [Formula: see text] sanitizer, which uses deep learning to improve the healthcare system. The proposed model is based on a Convolutional Neural Network with Bidirectional-LSTM and effectively performs Medical Entity Recognition. The experimental results show that [Formula: see text] sanitizer outperforms the state-of-the-art models with 91.14% recall, 92.63% in precision, and 92% F1-score. The sanitization model shows 28.77% improved utility preservation as compared to the state-of-the-art